System and method employing photokinetic techniques in cell biology imaging applications

ABSTRACT

A system and method employing photokinetic techniques in cell biology imaging applications are disclosed. Systems and methods of acquiring image data of an object may comprise: selectively inducing photoactivation of material at a site on the object; performing an optical axis integration scan; simultaneously executing a time delay integration scan sequence; and processing acquired image data in accordance with one or more desired analyses. Various methodologies and applications may include, inter alia, selective photobleaching of a site on the object, diffusion rate, velocity, and wave-front propagation analyses, multi-dimensional analyses of dispersion characteristics, biomolecular binding in cellular organelles, and photoactivation assisted systematic image segmentation for the study of cellular components.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a divisional of U.S. patent application Ser.No. 10/872,329, filed Jun. 18, 2004 now U.S. Pat. No. 7,408,176,entitled “SYSTEM AND METHOD EMPLOYING PHOTOKINETIC TECHNIQUES IN CELLBIOLOGY IMAGING APPLICATIONS” which is allowed and claims the benefit ofU.S. provisional application Ser. No. 60/480,432, filed Jun. 19, 2003,entitled “APPLICATIONS OF PHOTOKINETICS IN CELL BIOLOGY.” The presentapplication is also related to U.S. patent application Ser. No.10/389,269, filed Mar. 13, 2003, entitled “MULTI-AXIS INTEGRATION SYSTEMAND METHOD,” which issued as U.S. Pat. No. 7,283,253. The disclosures ofthe foregoing applications are hereby incorporated herein by referencein their entirety.

FIELD OF THE INVENTION

Aspects of the present invention relate generally to the field of cellbiology, and more particularly to a system and method employingphotokinetic techniques in various cell biology imaging applications.

BACKGROUND

Living matter is defined by more than the chemistry that governs thematter. Indeed, if one were to take the components of living matter(e.g., proteins, fats, sugars, nucleic acids, ions, etc.) and combinethose components ex vivo, one would not reconstitute or otherwise createlife. In fact, these components would generally tend to decompose intomore base elements due to entropy. In that regard, living matter may bedistinguished from the sum of its components by its ability to acquireenergy and to employ that energy in defeating entropy. Specifically,cells are capable of creating individual or discrete compartments inwhich differing or unique chemical environments (e.g., ionic, pH, etc.)are maintained. These successive compartments establish and maintainspecialized environments in which ordinarily unfavorable chemicalreactions become permissible. Living matter “defeats” entropy most oftenby moving chemical reactions from compartment to compartment, using theindividual compartments to create order out of chaos. Thus, life isdistinguished from inorganic matter in that living matter is capable ofemploying energy to construct order (such as proteins) from otherwisedisorganized building blocks (such as amino acids). In practice, ananalysis of cell dynamics is essential in developing an understanding ofcell biology.

Various methodologies have been developed to study cell dynamics. One ofthe more widely studied developments in this field involves thediscovery of a protein from Aequorea victoria, a jellyfish thatpopulates the Puget Sound region of Washington State. This protein isone of many found to fluoresce when exposed to deep blue light; thisparticular protein from Aequorea victoria is known as Green FluorescentProtein (GFP) because green light (e.g., in a range generally centeredaround a wavelength of approximately 510 nm) is emitted when the proteinis illuminated with deep blue light (e.g., in a range generally centeredaround a wavelength of approximately 410 nm).

As is generally understood in the art, GFP has a characteristic known as“self-assembly,” i.e., it will self-assemble into a fluorescent form,even when expressed as protein chimera with mammalian proteins inmammalian cells. This means that new proteins can be created where theGFP protein is merely a continuation of a native protein. This newprotein complex (the chimera) is fluorescent and permits thevisualization of the native protein in its native environment.

Since the discovery of GFP, molecular biologists have succeeded increating variants of the GFP protein, further optimizing its applicationin the study of mammalian cells. One such variant of GFP includes amodification of the absorption properties of the protein so that it isoptimally excited by light having a wavelength of approximately 488 nm.Another notable modification to GFP involved creation of a variant thatis only weakly fluorescent until it is activated by exposure to deepblue light having a wavelength of around 413 nm; once activated at thiswavelength, the GFP variant becomes about one hundred times morefluorescent (488 nm excitation, 510 nm emission) than it was prior toactivation.

Conventional technology is deficient at least to the extent that asystem and method have yet to be designed that are operative in concertwith, and take optimum advantage of, this photoactivated GFP (PA-GFP).

SUMMARY

Aspects of the present invention overcome the foregoing and othershortcomings of conventional technology, providing a system and methodemploying photokinetic techniques in various cell biology imagingapplications. In that regard, it will be appreciated that the term“photokinetic” in this context generally refers to a characteristic ormeasurement related to or indicative of changes in one or more aspectsof a chemical reaction, or to changes in the physical or chemicalcharacteristics of material, responsive to excitation light. Forexample, change in rate of a chemical reaction, alteration of movementsor dispersion of motile organisms, and other quantifiable responses maybe attributable to incident electromagnetic energy of a particularfrequency and wavelength. The “photokinetic” responses may be measuredin accordance with the systems and methods set forth herein.

In some exemplary embodiments, a method of acquiring image data of anobject comprises: selectively inducing photoactivation of material at asite on the object; performing an optical axis integration scan;simultaneously executing a time delay integration scan sequence; andselectively repeating the performing and the executing. The selectivelyinducing may generally comprise tagging the object to be scanned with aGreen Fluorescent Protein variant.

As set forth in more detail below, the performing may comprise acquiringimage data of the object at an image plane positioned along an opticalaxis, and may further comprise providing relative translation along theoptical axis of the object and the image plane. Similarly, the executingmay comprise providing relative translation along a lateral axis of theobject and the image plane. In accordance with some embodiments, theperforming further comprises selectively alternating a direction of therelative translation along the optical axis.

In some time delay integration scan sequences, the executing comprisessynchronizing the relative translation along the lateral axis with adata acquisition rate associated with an imaging device. In some opticalaxis integration scans, the performing further comprises integrating theimage data concomitantly with the acquiring. Embodiments of thedisclosed methods may additionally comprise deblurring the image datasubsequent to the integrating, deconvolving the image data subsequent tothe integrating, or both.

In accordance with some aspects of the present disclosure, exemplaryembodiments of a method of identifying a cellular structure maycomprise: selectively inducing photoactivation of material at a site onthe cell; observing dispersion of material activated responsive to theselectively inducing; and responsive to the observing, analyzingwave-front propagation to identify a cellular structure. The selectivelyinducing may generally comprise activating a Green Fluorescent Proteinvariant. Further, the selectively inducing generally comprisesdelivering excitation illumination having a selected wavelength. In someembodiments, the delivering may comprise pulsing the excitationillumination, and may additionally comprise selectively repeating thepulsing.

The observing may generally comprise utilizing using wide-field imaging.In some embodiments described with particularity below, the observingcomprises performing an optical axis integration scan and simultaneouslyexecuting a time delay integration scan sequence.

The analyzing may comprise application of a Fourier Transform;additionally or alternatively, the analyzing may comprise calculation ofiso-velocity values from successive images of wave-front propagation. Inaccordance with some embodiments, the analyzing comprises quantificationof anisotropic flow; in one specific embodiment, the quantificationidentifies the cellular structure.

In accordance with some exemplary embodiments, a method of analyzing abiomolecule comprises: inducing photoactivation of material at a site onthe biomolecule; photobleaching material at the site on the biomolecule;responsive to the inducing and the photobleaching, observing boundmaterial that is not diffusing within the biomolecule; and responsive tothe observing, identifying a compartmental structure of the biomolecule.Again, the inducing generally comprises activating a Green FluorescentProtein variant.

As set forth in more detail below, one of the inducing and thephotobleaching may comprise selectively pulsing excitation illuminationhaving a predetermined wavelength. In one embodiment, each of theinducing and the photobleaching respectively comprises selectivelypulsing excitation illumination having a respective predeterminedwavelength. The photobleaching may allow minimization of backgroundsignal for the observing.

The observing may comprise utilizing using wide-field imaging; inparticular embodiments set forth below, the observing comprisesperforming an optical axis integration scan and simultaneously executinga time delay integration scan sequence.

The identifying may comprise determining biomolecular transport intocellular organelles; additionally or alternatively, the identifying maycomprise determining biomolecular transport into cellular compartments.

The foregoing and other aspects of the disclosed embodiments will bemore fully understood through examination of the following detaileddescription thereof in conjunction with the drawing figures.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is a simplified functional block diagram illustrating oneembodiment of an image acquisition system operative in accordance withthe present disclosure.

FIG. 1B is a simplified functional block diagram illustrating a portionof the image acquisition system depicted in FIG. 1A.

FIGS. 2A and 2B are simplified diagrams illustrating the generaloperation of embodiments of image acquisition and image data processingmethods.

FIGS. 3A and 3B are simplified flow diagrams illustrating the generaloperation of the methods depicted in FIGS. 2A and 2B.

FIG. 4 is a simplified diagram illustrating the general operation ofshifts and readout functionality for a full frame CCD camera.

FIG. 5 is a simplified diagram illustrating one embodiment of time delayintegration synchronized with temporal events.

FIG. 6 is a simplified diagram illustrating aspects of wave frontdispersion analysis.

FIG. 7 is a simplified diagram illustrating one embodiment of a methodof studying flow directionality.

FIG. 8 is a simplified diagram of one embodiment of isolating aremaining fraction of a biomolecule tagged with a GFP variant.

FIG. 9 is a simplified diagram illustrating in situ protein half-times.

FIG. 10 is a simplified diagram illustrating one embodiment ofphotoactivation having utility in segmentation analyses.

FIG. 11 is a simplified diagram illustrating one embodiment ofphotoactivation having utility in methods of measuring molecularproximity using fluorescence resonance energy transfer.

DETAILED DESCRIPTION

Initially, it is noted that aspects of the disclosed embodiments arevariously directed to techniques involving time delay integration oftemporal events, image acquisition using optical axis integration, orboth. In that regard, systems and methods of time delay integration(TDI) and optical axis integration (OAI) are set forth in more detail inU.S. patent application Ser. No. 10/389,269, filed Mar. 13, 2003,entitled “MULTI-AXIS INTEGRATION SYSTEM AND METHOD,” the disclosure ofwhich is hereby incorporated herein by reference in its entirety.

Relevant portions of the foregoing application are reproduced below. Inparticular, it has been shown that a charge-coupled device (CCD) camera,a complementary metal oxide semiconductor (CMOS) detector, or similarapparatus employed in conjunction with an imaging system may be readrow-by-row in concert with stage or sample movement in order to optimizeacquisition rates. As set forth in more detail below, a similarmethodology may be employed to capture fast temporal events in aspatially invariant setting. By way of example, a laser may be used toilluminate a small area on the sample or other field to be imaged; asingle point detector, such as a photomultiplier tube (PMT), forexample, may have utility in recording light intensities from theilluminated region. Additionally, the point detector may be repositionedover the surface of the object to record signals from the entire imageplane. Further, a three-dimensional (3D) optical plane may be limited toan individual image plane by employing confocal apertures.

In that regard, it will be appreciated that image acquisition throughputoften represents the rate-limiting factor in systems and methods ofscanning high-content and high-throughput assays common in biomedical,cell biology, and other applications. Image acquisition throughput canbe especially problematic when an assay requires detection offluorescent probes, for example, and when high lateral resolution (inthe x and y dimensions) is required for high-content image analysisalgorithms. In cases where the detected signal is weak, such as influorescence imaging, for example, high numerical aperture (NA) lensesare generally used to maximize collection efficiency and to minimizeexposure time. A side effect of high NA lenses, however, is that thedepth-of-field (DOF, or the dimension of the in-focus region measured inthe z direction) is very shallow. As a consequence, high NA lenses havelimited ability to view thick objects, and are unable to follow unevensubstrates without refocus.

Even in cases where the detected signal is strong or is otherwise easilyacquired (such as transmitted visible light, for example) opticalsystems can still perform inadequately if the sample thickness isgreater than can be imaged by the optical DOF; additional imagingdifficulties can be introduced if the object to be imaged is not locatedin a plane orthogonal to the optical axis. These optical limitationsoften lead to the use of autofocus technology, or the need to acquireimages at more than one focal plane.

Although much effort has been invested in autofocus technologies,optical axis integration techniques are more cost effective andgenerally provide improved performance in many scanning applications.The scanning techniques set forth in detail below are very tolerant ofobjects having inconsistent or variable focal planes, for example, andmay be used to image thick objects. Additionally, scans performed inaccordance with the present disclosure may be faster than thoseimplementing autofocus or optical sectioning procedures.

Optical Axis Integration

A system and method operative in accordance with the present disclosureemploy optical axis integration (OAI) techniques as set forth in detailbelow. For a particular object to be imaged, for instance, rather thanattempting to determine a particular focal plane for optics or animaging apparatus (i.e., precisely determining an appropriate or optimalz position of the image plane), the object may be scanned along theoptical axis while a detector, computer, or other computationalapparatus concomitantly integrates the acquired images or image data.The resulting image is an integral (i.e., projection) of the image ofthe three-dimensional (3D) object along the optical axis. That is, anOAI image may generally be expressed as follows:

$\begin{matrix}{{i^{\prime}\left( {x,y} \right)} = {\int_{- \infty}^{+ \infty}{{i\left( {x,y,z} \right)}\ {\mathbb{d}z}}}} & 1\end{matrix}$

where i′ is the two-dimensional (2D) projection of a 3D image, i, alongthe optical axis (z direction).

In this context, the 3D image, I, can be described mathematically as theobject (o) of interest convolved with the point-spread-function (PSF) ofa microscope or other optical apparatus, as follows:

$\begin{matrix}{{i\left( {x,y,z} \right)} = {\int_{- \infty}^{+ \infty}{\int_{- \infty}^{+ \infty}{\int_{- \infty}^{+ \infty}{{o\left( {x^{\prime},y^{\prime},z^{\prime}} \right)}{{psf}\left( {{x - x^{\prime}},{y - y^{\prime}},{z - z^{\prime}}} \right)}\ {\mathbb{d}x^{\prime}}\ {\mathbb{d}y^{\prime}}\ {\mathbb{d}z^{\prime}}}}}}} & 2\end{matrix}$

Inserting equation 2 into equation 1 gives

$\begin{matrix}{{{i^{\prime}\left( {x,y} \right)} = {\int_{- \infty}^{+ \infty}{\int_{- \infty}^{+ \infty}{\int_{- \infty}^{+ \infty}{\int_{- \infty}^{+ \infty}{{o\left( {x^{\prime},y^{\prime},z^{\prime}} \right)}{{psf}\left( {{x - x^{\prime}},{y - y^{\prime}},{z - z^{\prime}}} \right)}\ {\mathbb{d}x^{\prime}}\ {\mathbb{d}y^{\prime}}\ {\mathbb{d}z^{\prime}}\ {\mathbb{d}z}}}}}}}\ } & 3\end{matrix}$

Rearranging the integration along the optical axis, z, then yields

$\begin{matrix}{{{i^{\prime}\left( {x,y} \right)} = {\int_{- \infty}^{+ \infty}{\int_{- \infty}^{+ \infty}{\int_{- \infty}^{+ \infty}{{o\left( {x^{\prime},y^{\prime},z^{\prime}} \right)}{\int_{- \infty}^{+ \infty}{{psf}\left( {{x - x^{\prime}},{y - y^{\prime}},{z - z^{\prime}}} \right){\mathbb{d}z}\mspace{7mu}{\mathbb{d}x^{\prime}}\ {\mathbb{d}y^{\prime}}\ {\mathbb{d}z^{\prime}}}}}}}}}\ } & 4\end{matrix}$

which is equivalent to

$\begin{matrix}{{{i^{\prime}\left( {x,y} \right)} = {\int_{- \infty}^{+ \infty}{\int_{- \infty}^{+ \infty}{\int_{- \infty}^{+ \infty}{{o\left( {x^{\prime},y^{\prime},z^{\prime}} \right)}{\int_{- \infty}^{+ \infty}{{psf}\left( {{x - x^{\prime}},{y - y^{\prime}},z} \right){\mathbb{d}z}\mspace{7mu}{\mathbb{d}x^{\prime}}\ {\mathbb{d}y^{\prime}}\ {\mathbb{d}z^{\prime}}}}}}}}}\ } & 5\end{matrix}$

Rearranging the integration along z′, the OAI image, I′(x,y), may beexpressed as:

$\begin{matrix}{{{i^{\prime}\left( {x,y} \right)} = {\int_{- \infty}^{+ \infty}{\int_{- \infty}^{+ \infty}{\int_{- \infty}^{+ \infty}{{o\left( {x^{\prime},y^{\prime},z^{\prime}} \right)}{\mathbb{d}z^{\prime}}{\int_{- \infty}^{+ \infty}{{psf}\left( {{x - x^{\prime}},{y - y^{\prime}},z} \right){\mathbb{d}z}\mspace{7mu}{\mathbb{d}x^{\prime}}\ {\mathbb{d}y^{\prime}}}}}}}}}\ } & 6\end{matrix}$

Equation 6 shows that an OAI image, I′(x,y), may be expressed as theconvolution of the integral of the object along the optical axis withthe integral of the PSF along the optical axis. Equation 6 is alsoillustrative of the relationship between the projection of the object,the projection of the image, and the projection of the PSF along theoptical axis.

The following definitions may facilitate further simplification of theforegoing formulation:

$\begin{matrix}{{{o^{\prime}\left( {x,y} \right)} = {\int_{- \infty}^{+ \infty}{{o\left( {x,y,z} \right)}\ {\mathbb{d}z}}}}{{{psf}^{\prime}\left( {x,y} \right)} = {\int_{- \infty}^{+ \infty}{{{psf}\left( {x,y,z} \right)}\ {\mathbb{d}z}}}}} & 7\end{matrix}$

Inserting the definitions expressed above into Equation 6 yields

$\begin{matrix}{{i^{\prime}\left( {x,y} \right)} = {\int_{- \infty}^{+ \infty}{\int_{- \infty}^{+ \infty}{{o^{\prime}\left( {x,y} \right)}{{psf}^{\prime}\left( {{x - x^{\prime}},{y - y^{\prime}}} \right)}\ {\mathbb{d}x^{\prime}}\ {\mathbb{d}y^{\prime}}}}}} & 8\end{matrix}$

The best method of solving Equation 8 for o′(x,y) involves FourierTransforms, and is a well known procedure. Applying a Fourier Transformto both sides of Equation 8 and applying the convolution theorem (see,e.g., Bracewell, 1986) results in the following relationship:I′(u,v)=O′(u,v)OTF′(u,v)  9

Capital letters have been used to denote the Fourier Transform of thecorresponding functions, and the Fourier Transform of the PSF has beenreplaced with the conventional term for its Transform, the opticaltransfer function (OTF). Rearranging terms and performing an inverseFourier Transform then yieldso′(x,y)=F ⁻¹ [I′(u,v)/OTF′(u,v)]  10

where F⁻¹ represents the inverse Fourier Transform.

Equation 10 describes an efficient method of calculating a 2D projectionof an object from a projection of the image and a projection of theoptical PSF. A single-step solution may work well with good qualityimages; for lower quality images, however, an iterative solution ofEquation 10 may yield a more reliable result. See, e.g., the constrainediterative technique described by Agard et al. (David A. Agard and JohnW. Sedat, Nature, volume 302, 1984, pages 676 et seq.).

As described and contemplated in the present disclosure, a system andmethod may implement, incorporate, or comprise OAI techniques in eitherof two forms: digital; or analog. In embodiments incorporating orpracticing digital OAI, for example, a series of images may be collectedalong the optical axis and then digitally summed to form I′(x,y). Thissummation may occur during or after the scan, i.e., it may not benecessary to save individual optical sections as discrete images orcollections of image data. In analog OAI embodiments, for example,I′(x,y) may be generated by scanning the object along the optical axiswhile the image data are accumulated within a CCD camera, a CMOSdetector, a PMT, or another type of detector. The integration may beperformed in the imaging apparatus or detector and generally may resultin only a single image, i.e., a single image may represent the entiredepth of the object in the z direction along the optical axis.

Analog OAI may have particular utility with respect to operationsinvolving scanning microarrays, for example, with CCD cameras, CMOSdevices, or other detectors. A system and method employing analog OAImay eliminate or substantially reduce reliance upon sophisticated,time-consuming, and processor intensive autofocus procedures.

In many applications, analog OAI may provide a number of advantages overdigital OAI and autofocus, especially for automated scanners. Forexample, as compared with digital OAI, the analog OAI embodiments:require substantially lower data collection and processor overhead;exhibit lower read noise; and exhibit lower photon noise for equivalentexposure times.

As compared with traditional autofocus systems, advantages of the analogOAI embodiments may include the following: faster scan times; lowertotal exposure requirements; minimization or elimination of problemsrelated to determining an arbitrary plane of focus; and integration ofthe 3D object yields or allows full quantitation of the object, i.e.,the information content of the OAI image is higher than that achievedwith autofocus systems, and accordingly, fewer structures associatedwith the object of interest are missed.

As compared with the analog technology, advantages of digital OAIembodiments may include a potential for achieving substantially largerphoton counts; accordingly, 3D images may be made available for advancedimage analysis such as 3D deconvolution, volumetric measurements, andthe like.

The synergistic combination of the OAI techniques described above withdeconvolution, for example, may provide a significant advance forautomated slide scanning techniques. For instance, OAI images generallymay benefit from the quantitative deblurring procedure; similarly,deconvolution performance may be improved because Equation 10 deals withimages in 2D rather than 3D. Furthermore, many forms of image analysesbased upon images obtained from autofocused systems will work equallywell (or better) with projected images.

For example, a basic object detection operation may benefit from OAIimage processing techniques; in that regard, it will be appreciated thatimages with minimal DOF (i.e., autofocus images) are less likely tocontain a specific object of interest than the corresponding projectionimage. Likewise, analyses that use intensity integration may alsobenefit from application of OAI techniques, at least because the zdimension (i.e., along the optical axis) is already integrated into theOAI result. By way of another example, assays that integrate intensitieswithin 3D structures (e.g., nucleus, cytoplasm, and endoplasticreticulum) may generally be more accurate with OAI images because 2Dautofocus images cannot properly measure out-of-focus intensities.

Turning now to the drawing figures, FIG. 1A is a simplified functionalblock diagram illustrating one embodiment of an image acquisition systemoperative in accordance with the present disclosure, and FIG. 1B is asimplified functional block diagram illustrating a portion of the imageacquisition system depicted in FIG. 1A. Those of skill in the art willappreciate that FIGS. 1A and 1B are provided by way of example only, andthat the specific arrangement of components is susceptible of numerousmodifications; the exemplary scale, orientation, and interrelationshipof the various components may be altered in accordance with systemrequirements. Additionally, as will become apparent from examination ofthe following description, some or all of the functionality of somecomponents depicted as discrete elements may be combined or incorporatedinto other components.

System 100 generally comprises a microscope 110 operably coupled to aprecision movable stage 120 and to an image acquisition component 140;stage 120 may be configured and operative to support a microarray,microscope slide, or other similar structure (reference numeral 190)upon which a specimen or object 199 to be imaged is disposed. As isgenerally known in the art, microscope 110 may comprise, or be operativein conjunction with, an illumination source 111 for illuminating stage120, slide 190, or both with light of a predetermined or selectedfrequency or spectral bandwidth; in that regard, illumination source 111may provide light in the visible, infrared, or ultraviolet wavelengths.

In some embodiments, illumination source 111 may be incorporated withinhousing 112 of microscope 110, i.e., on the opposite side of stage 120and slide 190 than depicted in FIG. 1A. Alternatively, an additionalsource of illumination (not shown) to be used in conjunction with, or inlieu of, source 111 may be accommodated or maintained in housing 112. Inthese embodiments, any such illumination source disposed within housing112 may be suitably dimensioned and positioned neither to interfere withoptical components of microscope 110 nor to obstruct the optical paththrough microscope 110 (to image acquisition component 140).

As noted above, stage 120 may be movable relative to optics (e.g.,objective 119 illustrated in FIG. 1B) incorporated into microscope 110(microscope optics are not depicted in FIG. 1A). In some embodiments,stage 120 may be movable in both the x and y directions (where the yaxis is normal to the plane of FIGS. 1A and 1B). In this context, boththe x axis and the y axis may generally be referred to herein as“lateral” axes, and may describe a plane orthogonal to the optical axis(described below) of system 100. Additionally or alternatively, stage120 may incorporate or comprise one or more structures and mechanismsconfigured and operative precisely to position slide 190 laterally inthe x and y directions relative to the structure of stage 120 itself. Insuch embodiments, precise 2D lateral positioning (i.e., x and ycoordinates) of object 199 relative to the optical path of microscope110 may be achieved through movement of stage 120 relative to microscopeoptics, movement of slide 190 relative to stage 120, or both.

In some embodiments, stage 120 may also be movable along the z axis (theoptical axis). It will be appreciated that microscope optics may alsofacilitate positioning an object on slide 190 in the proper location in3D space (i.e., x, y, and z coordinates) relative to the optical pathand the focal point of objective 119. In that regard, one or moreoptical components of microscope 110 such as objective 119 may bemovable in the z direction, either in addition to, or as an alternativeto, selectively moving stage 120 along the optical axis. Additionally oralternatively, objective 119 may be movable along the x axis, the yaxis, or both.

It will be appreciated that numerous mechanisms and methods ofpositioning object 199 to be imaged relative to microscope optics aregenerally known. Relative movement of various components (such as slide190, stage 120, and objective 119, for example), either individually orin combination, may vary in accordance with system requirements andconfiguration, and may be effectuated to position object 199 in asuitable location relative to objective 119. The present disclosure isnot intended to be limited by the structures and processes employed toposition object 199 relative to objective 119 and the optical path orthe image plane. Accordingly, reference made herein to relative motionof object 199 and an image plane may generally comprise movement ofobject 199, movement of the image plane, or some combination of both.

Microscope optics may generally be configured and operative inconjunction with image acquisition component 140; in that regard,component 140 generally comprises a camera, a CCD imager, a CMOS device,a PMT, or some other detector 141 operably coupled to an image processor142 or other appropriate electronics. System 100 may additionallyinclude control electronics 150 operative to control, for example:operational parameters, functional characteristics, or otherconfigurable aspects of image processor 142 and detector 141; two- orthree-dimensional motion of stage 120, objective 119, or othercomponents; power output, spectral bandwidth, frequencies, or otherparameters for source 111 and any other illumination source incorporatedinto system 100; data storage; and the like. In that regard, electronics150 may comprise one or more microprocessors, microcontrollers, or otherprogrammable devices capable of executing computer readableinstructions; additionally, electronics 150 may also comprise or beoperably coupled with data storage media or networked devices such asfile servers, application servers, and the like. Those of skill in theart will appreciate that various methods and apparatus employingmicroprocessors or computer executable instruction sets to configure andto control operation of image acquisition systems are generally known.

In operation, image data acquired by detector 141 may be summed,manipulated, saved, or otherwise processed by hardware, software, orboth resident at image processor 142; in some embodiments, functionalityof processor 142 may be influenced or controlled by signals transmittedfrom electronics 150 as noted above. Alternatively, the functionality ofimage processor 142 and electronics 150 may be incorporated into asingle device, for example. Specifically, image processor 142 may beoperative in accordance with instruction sets to compute solutions orapproximations for the equations set forth herein.

FIGS. 2A and 2B are simplified diagrams illustrating the generaloperation of embodiments of image acquisition and image data processingmethods, and FIGS. 3A and 3B are simplified flow diagrams illustratingthe general operation of the methods depicted in FIGS. 2A and 2B.

FIGS. 2A and 3A generally illustrate one conventional approach to imageprocessing operations. As indicated at block 311, a series, or stack, of2D images is acquired in sequential x,y planes (i.e., optical sections)along the z axis. The resulting image, i(x, y, z), is expressedmathematically at Equation 2 above, which is computationally expensiveto solve. As illustrated in FIG. 2A, the deconvolution operationdepicted at block 312 is executed with respect to the entire stack ofoptical sections, and is accordingly inefficient andprocessor-intensive; since each optical section includes data from othersections (due to DOF range, for example), the deconvolution operationprocesses more data than required. Finally, the deconvolved 3D image isprojected into 2D image, o′(x,y), as indicated at block 313.

FIGS. 2B and 3B generally illustrate a significantly improved approachto image processing operations as contemplated herein. As in the FIG. 3Aembodiment, a series, or stack, of 2D images may be acquired insequential x,y planes (i.e., optical sections) along the z axis (block321). The resulting image, i(x, y, z), is expressed mathematically atEquation 2 above, which is computationally expensive to solve. Asillustrated in FIG. 2B and indicated at block 322, the stack of opticalsections may be projected into 2D image, I′(x,y), prior todeconvolution; this image is expressed mathematically at Equation 8above, which is a substantially simplified, 2D version of Equation 2.The deconvolution operation depicted at block 323 results in the same 2Dimage, o′(x,y), as the embodiment described above with reference toFIGS. 2A and 3A; the FIG. 3B embodiment generates the deconvolvedprojection at a significant savings in computational overhead, however,since the processor-intensive deconvolution is executed in only twodimensions.

Time Delay Integration

As used herein, the phrase “time delay integration” (TDI) generallyrepresents a method of continuous scanning which may be implemented inconjunction with CCD cameras, CMOS devices, PMT apparatus, or otherimaging devices. In CCD cameras and similar apparatus, for example,incident light creates electric charge at individual charge-coupledwells on the device surface. Charged electrons are then transferredsequentially down the columns of the chip (parallel shifts) while therow that reaches the bottom of the chip is transferred to an accumulatorcalled the serial register. The serial register is then shiftedhorizontally and processed by an A/D converter.

In accordance with some TDI embodiments, precision motion control may beemployed to synchronize motion of the object being imaged or motion ofthe camera or other imaging device (as set forth above with reference toFIG. 1A) with motion of the charged electrons across the CCD or imagingdevice surface. Relative translation of the object and the image planealong the lateral axis may be controlled such that a particular portionof the imaged object tracks down the chip as the electrons representingthat particular portion of the image are shifted down the chip. As setforth in detail above, such relative translation may comprise motion ofthe object, motion of the image plane, or both. Accordingly, the objectmay be continuously imaged as it passes down the chip. TDI methodologiesmay facilitate or enable efficient scanning of, among other things,fluorescent DNA microarrays, for example, and may have utility invarious other applications related to scanning myriad biologicalspecimens.

FIG. 4 is a simplified diagram illustrating the general operation ofshifts and readout functionality for a full frame CCD camera. In thatregard, FIG. 4 provides a simple demonstration of slow-scan CCD cameraread operations. Individual pixel electrons are shifted in parallel(e.g., down the columns to successive rows) to a predetermined portionof the chip (e.g., the bottom of the chip in FIG. 4). Image data at thebottom row are shifted off of the chip onto the serial register, whichis, in turn, shifted horizontally to the readout amplifier to create avoltage that is digitized to form a digital image.

It will be appreciated that the FIG. 4 embodiment is provided forillustrative purposes only, and that various CCD cameras, CMOS devices,PMT apparatus, or other imaging devices may be characterized byalternative operational features, particularly with respect to theexemplary geometry. For example, the operation of some CCD cameras mayexecute parallel shifts oriented at 90 or 180 degrees from thosedepicted in FIG. 4.

FIG. 5 is a simplified diagram illustrating one embodiment of time delayintegration synchronized with temporal events. In that regard, FIG. 5illustrates a precise motion control TDI implementation which may beemployed in fluorescence imaging systems, for example, or in numerousother imaging applications. In accordance with the exemplaryembodiments, a given location on the specimen (“point of illumination”in FIG. 5) may be moved (either relative to the imagining device, forexample, or relative to the image plane of the system) in synchrony withthe parallel shifts such that V_(P) (i.e., the parallel shift velocity)is equal to V_(Y) (i.e., the shift velocity of the point ofillumination). In the foregoing manner, the specimen may be imagedthroughout the period of time that it takes for an entire chip to beread by the camera.

In this context, synchronous motion between the object and the CCD rowmay be effectuated substantially as set forth in detail above withreference to FIG. 1A. Relative motion of slide 190, stage 120, variousoptical components, or some combination thereof, for instance, mayaccurately position the point of illumination depicted in FIG. 5 in asuitable location for imaging during the scan. The degree ofsynchronicity, the velocities imparted to the mechanical components, theprecision with which the object must be positioned for a desired imagingquality, mechanical settling time, backlash, and the like may vary inaccordance with system configuration, and may be application dependent.

Upon examination of FIGS. 4 and 5, it will be readily apparent thatrelative translation of the object and the image plane along the lateralaxis as set forth above may be synchronized with the rate at which dataare acquired and read from the detector. Accordingly, it is noted thatthe capabilities of the imaging device (such as detector 141) and othercomponents of the optical system (such as, inter alia: the maximumresolution and the time required to focus the imaging device; and NA andDOF of the optics) may affect the behavior and performance of the systememploying the FIG. 5 TDI embodiment.

Consistent with the foregoing description, aspects of the presentinvention are related to use of row-by-row reading of a CCD, CMOS, PMT,or similar detector to facilitate fast sampling rates (as fast asapproximately 1 ms or better, for example) of a single point, i.e., TDIreading of a CCD camera or other imaging device in conjunction withsingle point or single row (targeted) illumination techniques may enablefast acquisition of time-resolved data. This time-resolved informationmay be used for spectroscopic examination in such applications asFluorescence Life-time Imaging, Fluorescence Recovery AfterPhotobleaching, Photoactivation, Rotational Diffusion Measurements, andmany other applications. Since the methodology may inherently becombined with traditional imaging, the same detector (whether based uponCCD, CMOS, or other technology) may be used to image an entire field(such as a cell) as well as for site-specific spectroscopic examination.

Analysis of Dispersion Wave-Fronts Subsequent to Photoactivation

FIG. 6 is a simplified diagram illustrating aspects of wave frontdispersion analysis. Specifically: FIG. 6A is a simplified diagramillustrating simple isotropic dispersion from a single activation site;FIG. 6B is a simplified diagram illustrating anisotropic dispersion anddepicting non-uniform impedance to diffusion vertically relative tohorizontally; FIG. 6C is a simplified diagram illustrating the FIG. 6Bdispersion quantified and converted into an iso-velocity plot; and FIG.6D is a simplified diagram illustrating one embodiment of a method ofuncovering hidden cellular structure employing iso-velocity plots anddispersion analysis.

Variants of PA-GFP have been used in conjunction with a confocalmicroscope for both activation and subsequent, i.e., post-activation,imaging. The confocal microscope previously employed was limited in thespeed at which subsequent frames were acquired. Imaging limitations, inturn, inherently limit the temporal resolution of such instruments. Inaccordance with the present disclosure, however, rapid and non-isotropic(or “anisotropic”) dispersion of protein chimeras may be observed usinglaser induced photoactivation and subsequent wide-field imaging. Suchobservation of anisotropic dispersion may facilitate detailed analysisof the flow patterns of proteins within cellular compartments andbetween those compartments. While complementary with fluorescencerecovery after photobleaching experiments, this analysis is unique inthat it is able to divulge patterns of flow from the activation site.Specifically, the use of anisotropic flow characteristics may revealflow resistant structures within cells as indicated in FIG. 6D.

Additionally, aspects of the disclosed embodiments involve applicationof waveform or wave-front propagation analyses to the study of cellularstructure. In some instances, these analyses may be embodied in orcomprise construction of iso-velocity graphs of dispersion. Inaccordance with the present disclosure, an iso-velocity map may beconsidered analogous to a topographical map where the lines ofequivalent velocity of dispersion are mapped instead of elevation. Morecomplex analyses involving the Fourier Transform of these waveforms mayreveal high-resolution maps of flow patterns within cells. As notedabove, FIG. 6B is a simplified diagram illustrating anisotropicdispersion; non-uniform impedance to diffusion is represented by theflattened area of dispersion in the third and fourth panels in FIG. 6B.This anisotropic or non-uniform dispersion may be quantified subsequentto rapid wide-field imaging, some embodiments of which may employ one ormore of the techniques set forth above. Iso-velocity plots, i.e.,depicting contours representing locations where dispersion proceeds atsubstantially equivalent velocities, are illustrated in FIGS. 6C and 6D.As indicated in FIG. 6D, iso-velocity plot contours and wave-frontdispersion analyses may facilitate identification of cellular structurethat impedes dispersion.

Rapid Three-Dimensional Studies of Dispersion Subsequent toPhotoactivation

Methodologies for rapidly acquiring images from 3D space using OAItechniques are disclosed in U.S. patent application Ser. No. 10/389,269,and set forth in detail above. These methods provide means of acquiringsummary data of 3D volumes rapidly, and may be combined withphotoactivation and photobleaching to achieve estimates of 3Danisotropic diffusion as well as 3D diffusion constants, even in rapidlydiffusing substances. In that regard, FIGS. 2B and 3B illustrate thegeneral operation of one embodiment of an image acquisition and imagedata processing method employing the principle of OAI.

Specifically, the FIG. 2B embodiment acquires a stack of 2D images insequential x,y planes along the z axis. The resulting image, i(x, y, z),may be expressed mathematically in accordance with Equation 2 set forthabove. The stack of optical sections may be projected into a 2D image,I′(x,y), prior to deconvolution; this image may then be expressedmathematically in accordance with Equation 8, generating a deconvolvedprojection at a significant savings in processing overhead.

As set forth above, an OAI method may involve collecting an extendedfocus image either through digital or analog integration. The extendedfocus image (optical axis integrated) may then be deconvolved digitally.The result of such acquisition and data processing is a high-resolutionimage that accurately represents the sum of image intensities across alarge DOF. In some embodiments, OAI may be implemented in concert withmoving the stage (or the optics, or both) laterally, as in TDImethodologies, so as to acquire a large field of view with high lateralresolution as well as a large DOF.

It will be appreciated that a combination of OAI and TDI imagingtechniques may have particular utility in the post-activation wave-frontpropagation analyses set forth above with reference to FIG. 6.Specifically, in accordance with some embodiments, a method of acquiringimage data of an object may comprise: selectively inducingphotoactivation of material at a site on the object; performing anoptical axis integration scan; simultaneously executing a time delayintegration scan sequence; and selectively repeating the performing andthe executing to image the object and, optionally, to acquire wave-frontpropagation data.

Unidirectional Diffusion and Flow

Most models of diffusion and flow within living organisms assume thatdiffusion is bidirectional. The classic models assume that the mixing ofchemical species proceeds randomly from a heterogeneous distribution toa homogeneous distribution. These assumptions are justified, in part,because they make many calculations much simpler and, in part, becausemethods to test alternative hypotheses have heretofore been unavailable.One way that these assumptions may be tested is by activating a pool ofPA-GFP tagged protein and subsequently, before the pool is homogeneouslydistributed within the cell or compartment, depleting the remainingactivated population at the activation site. If diffusion is trulybidirectional, the depleted site at the center of the activation willfill back in at the same rate as the leading edge of the diffusion awayfrom the activation site. If structures that create non-uniformdiffusion exist, then the foregoing rates will differ.

The existence of such unidirectional channels may suggest that diffusionis controlled in ways that have yet to be considered within the cell.Such channels may be considered as playing an important role in diseaseprocesses such as bacterial invasion and compartmentalization.Additionally, control of such unidirectional flows should providesignificant targets for pharmaceutical intervention and may be used forderiving new drug classes for antibiotics, antiviral agents,chemotherapeutics, and neuro-pharmaceutical agents.

FIG. 7 is a simplified diagram illustrating one embodiment of a methodof studying flow directionality. A spot of light (electromagnetic energyhaving predetermined or desired frequency, wavelength, and intensitycharacteristics) may activate the PA-GFP (as indicated at time=T₀). Theactivated PA-GFP may then be allowed to disperse (as indicated attime=T₁). At time=T₂, the same site selected for activation may bephotobleached, for example, using a 488 nm laser (other wavelengths maybe employed, for instance, depending upon system requirements, thecomposition of the fluorescent compound, and other factors). Therecovery of fluorescence may then be monitored at the bleach site. Ifthe flow is bidirectional, then the bleach spot may be expected torecover (bidirectional dispersion). If the flow is unidirectional, onthe other hand, the bleach spot may be expected not to recover(unidirectional dispersion).

Unidirectional Molecular Incorporation into Organelles

Organelles are compartmental structures within cells; the organellesrepresent the microenvironments that the cell uses to create so called“uphill” reactions, i.e., those reactions involved in the synthesis ofmolecular species that involve an increase in organization within thecell. Examination of the movement of proteins and biomolecules into andout of these compartments may facilitate understanding of proteindynamics. In some cases, however, the organellar transport is a smallfraction of the total mass of biomolecule, rendering imaging andmeasuring the organellar fraction difficult.

In accordance with the present disclosure, however, a method ofmeasuring this fraction is possible. For instance, PA-GFP attached to abiomolecule may be activated; following a predetermined “wait” period orduration, for example, a focused spot of light suitable for exciting theactivated PA-GFP may be introduced away from the area of study, forinstance, at a distal portion of the cell. The foregoing technique mayeffectively photobleach the “free” component of the PA-GFP biomolecule,eliminating it from the measurement. In some embodiments, the remainingfraction of the PA-GFP labeled biomolecule may be bound orcompartmentalized. That remaining fraction may be studied without theinterference of the mobile fraction.

In that regard, FIG. 8 is a simplified diagram of one embodiment ofisolating a remaining fraction of a biomolecule tagged with a GFPvariant. In the FIG. 8 embodiment, PA-GFP may be repeatedly activateduntil the cell is largely filled with activated PA-GFP; this repeatedactivation may be followed by repeated photobleaching until most or allof the freely diffusing PA-GFP is bleached. Compartmentalized or boundGFP that is not freely diffusing may be retained by the cell and used tovisualize compartmental dynamics and to obtain measurements ofcompartmental characteristics.

Accordingly, a method of analyzing a biomolecule as set forth herein maycomprise: inducing photoactivation of material at a site on thebiomolecule; photobleaching material at the site on the biomolecule;observing bound material that is not diffusing within the biomolecule;and responsive to the observing, identifying a compartmental structureof the biomolecule.

Protein Lifetime Studies

One aspect of the study of biomolecules involves examination of what isconventionally characterized as the “turnover rate.” In particular, thehalf-life of a molecule is especially important in some studies ofreceptor binding and effector molecules. As set forth in more detailbelow, aspects of the present invention involve methodologies relatingto measuring biomolecule half times. In some exemplary embodiments, theprotein may be photoactivated. Since the activation of PA-GFP involves anon-reversible reaction between two amino acids, the fluorescence itselfis non-reversible. Thus, the fluorescence half-time is equal to thetotal loss of fluorescence over time minus the loss due tophotobleaching and the variability of the illumination intensity. If theinput or excitation illumination intensity is measured with aphotosensor, and if the photobleaching fraction can be quantified, thenthe total change in fluorescence attributable to the protein half-lifemay readily be computed. In that regard, FIG. 9 is a simplified diagramillustrating in situ protein half-times.

Photoactivation of GFP to Aid Image Segmentation

Computer based imaging is facilitating the conversion of cell biologyfrom a descriptive, or “qualitative,” discipline to a quantitativediscipline. In that regard, clear delineation or designation of theboundaries between cells, organelles, and their environments may haveparticular utility in quantitative cellular analyses. In order tofacilitate such designations, computer algorithms have been created thatseek to “segment” or otherwise to divide the image between thoselocations that fall within a given cell or structure and those that falloutside. In that regard, acquired signals may be quantified, forexample, as a function of time, cell cycle, disease state, and so forth.The identification of those locations that fall within and without thecell and its structures may represent the most difficult of imagingtasks in many applications; the challenge is to identify structures withparticularity (and to increase or amplify signals representative of thestructures) while diminishing the signal of the background.

In accordance with the present disclosure, PA-GFP, either attached toorganelle specific proteins or randomly expressed, may be employed tofacilitate location and identification of particular cells or attendantstructural components. The organellar regions may be activated bytargeting the area under analysis with excitation illumination of anappropriate wavelength employing, for example, a light source generatinglight having a wavelength of approximately 413 nm. These organelles maythen be clearly discernable from background signals based upon thepresence of PA-GFP signal. This increased contrast (signal tobackground) may facilitate automated segmentation of the acquired image.Once the boundaries of the cell or organelle are delineated, other colorchannels may be used to mark proteins and structures of interest forsubsequent quantification.

A variant of this methodology may be used in developmental biology. Acritical methodology in the discipline of cell biology is the study ofcell lineage, i.e., identifying controls and signals that may determinethe fate of cells as the organism develops from a single or few-celledorganism to maturity. Conventional cell lineages are difficult toperform and require hundreds of man-hours. In accordance with thepresent disclosure, individual cells in blastocysts and embryos may bemarked by photoactivation. Since the activation of PA-GFP isnon-reversible, these cells and their progeny will remain fluorescentuntil either the activated protein is sufficiently diluted out bynon-activated protein or until the proteins are actively degraded by thecell. These activated cells may be followed automatically using one ormore imaging techniques substantially as set forth above.

In that regard, FIG. 10 is a simplified diagram illustrating oneembodiment of photoactivation having utility in segmentation analyses.As indicated at panel A in FIG. 10, individual cells may be difficult todelineate, and tracking one particular cell may be virtually impossiblewithout the beneficial effects of selective photoactivation. Asindicated at panel B in FIG. 10, however, one cell has been specificallymarked by photoactivation. That individual cell, having been tagged inaccordance with the procedure set forth above, may be much easier toseparate optically from its neighbors for segmentation; additionally,movements over time are more easily observed.

Photoactivation of GFP in the Study of Fluorescence Resonant EnergyTransfer (FRET)

Cellular activity is controlled by the interactions of biomolecules,most notably, proteins. In order for proteins to interact, they mustgenerally be close to each other (typically within 5-10 Å) at distanceswhich may be too small to measure directly with optical methods;methodologies other than optical techniques must generally be employedin that regard. One common technique for observing or otherwisemonitoring biomolecule interactions is generally referred to asFluorescence Resonant Energy Transfer, or “FRET.” In accordance withconventional FRET techniques, two fluorescent molecules approach eachother in close enough proximity that the excitation of one molecule (the“donor”) yields fluorescent emission from the other (the “acceptor”). Asis generally known in the art, this energy transfer is a quantummechanical event that involves the sharing of energized electronsbetween the two molecules. In theory, the existence of FRET can bemonitored, for instance, by exciting the donor and looking for emissionstypical of the acceptor. In practice, however, it is difficult to knowwith certainty that the acceptor emission is actually caused by donorabsorption and FRET influences, or whether it is an artifact of acceptorexcitation and emission independent of FRET.

In accordance with the foregoing description, a method of FRET isenabled whereby the acceptor absorption and emission may be measured inthe presence of the donor molecule, but without fluorescence of thedonor molecule. Upon measuring the acceptor localization andintensities, the donor molecule (PA-GFP) may be initiated by activatingthe PA-GFP. At this point, enhancement of acceptor signal may beinterpreted as due to the presence of the donor and FRET influences. Theforegoing procedure may be used as a calibration or correction for FRETmeasurements. FIG. 11 is a simplified diagram illustrating oneembodiment of photoactivation having utility in methods of measuringmolecular proximity using fluorescence resonance energy transfer.Specifically, FIG. 11 demonstrates how the combination of two proteins,each having an appropriate fluorochrome attached, may yield a complexthat exhibits FRET effects.

It will be appreciated that the foregoing methodologies are susceptibleof myriad modifications and have utility in numerous applications. Thedescription set forth above, for example, may enable efficientrow-by-row reading of a CCD or similar apparatus in concert with singlepoint or row illumination for fast data acquisition of time-resolveddata; those of skill in the art will appreciate that the addition of adiffraction grating may enable utilization of substantially the entirelength along a detector row for spectral information. As set forthabove, the CCD or other imaging device may, additionally oralternatively, be used for traditional wide-field imaging processes.

In some embodiments, pulsed point activation and rapid wide-fieldimaging may be implemented to study dispersion characteristics ofactivated fluorescent materials. Specifically, the application ofwave-front propagation analytical tools may enable detailed study ofcellular structure; as set forth above, the application of FourierTransforms may facilitate such investigations of wave-front propagation.In some embodiments, the calculation of iso-velocity values fromsuccessive images of wave-front propagation may have particular utilityin photoactivation experiments. Such iso-velocity maps of wave-frontpropagation may be displayed independently, for example, orsimultaneously (or otherwise in combination) with image intensity data.

Given the foregoing detailed description, multi-dimensional dataacquisition techniques may be applied to the analysis of dispersioncharacteristics subsequent to photoactivation. Specifically, multipleaxis integration methods, such as OAI and TDI, may facilitate dataacquisition and enable detailed study of such dispersion.

In some embodiments, the combination of photoactivation andphotobleaching techniques may be implemented to study anisotropic flowdirections in cell biology. Such anisotropic flow evaluations may beimplemented in conjunction with the study and testing of host invasionin cell biology, the development and testing of antibiotics using cellbiology, the development and testing of antiviral agents using cellbiology, the development and testing of chemotherapeutic agents usingcell biology, the development and testing of neuro-pharmaceutics usingcell biology, and other applications.

As described above, photobleaching procedures may allow or facilitateremoval of background signal in the study of cellular structure.Specifically, application of photoactivation processes and subsequentphotobleaching processes may be implemented for the study ofbiomolecular transport into cellular organelles and compartments, thestudy of biomolecular binding in cellular organelles and compartments,and other applications.

Based upon the foregoing, it will be appreciated that the disclosedmethods may implement PA-GFP to enable or to facilitate the study of invivo protein lifetimes in cell biology. In some embodiments, applicationof photoactivation processes may aid in systematic image segmentationfor the study of cellular components; such image segmentation may alsobe employed for the study of cell migration in developmental biology, inchemotaxis, in diapedesis and immunology, and other fields.Additionally, PA-GFP may be employed in the study of protein proximitywhen used in conjunction with FRET techniques.

Aspects of the present invention have been illustrated and described indetail with reference to particular embodiments by way of example only,and not by way of limitation. It will be appreciated that variousmodifications and alterations may be made to the exemplary embodimentswithout departing from the scope and contemplation of the presentdisclosure. It is intended, therefore, that the invention be consideredas limited only by the scope of the appended claims

1. A method of identifying a cellular structure; said method comprising:selectively inducing photoactivation of material at a site on the cell;observing dispersion of material activated responsive to saidselectively inducing; and responsive to said observing, analyzingwave-front propagation to identify a cellular structure.
 2. The methodof claim 1 wherein said selectively inducing comprises deliveringexcitation illumination having a selected wavelength.
 3. The method ofclaim 2 wherein said delivering comprises pulsing said excitationillumination.
 4. The method of claim 3 wherein said delivering comprisesselectively repeating said pulsing.
 5. The method of claim 1 whereinsaid observing comprises utilizing using wide-field imaging.
 6. Themethod of claim 1 wherein said observing comprises performing an opticalaxis integration scan and simultaneously executing a time delayintegration scan sequence.
 7. The method of claim 1 wherein saidanalyzing comprises application of a Fourier Transform.
 8. The method ofclaim 1 wherein said analyzing comprises calculation of iso-velocityvalues from successive images of wave-front propagation.
 9. The methodof claim 1 wherein said analyzing comprises quantification ofanisotropic flow.
 10. The method of claim 9 wherein said quantificationidentifies the cellular structure.
 11. The method of claim 10 whereinsaid selectively inducing comprises activating a Green FluorescentProtein variant.